In this report we will analyze the StackOverflow 2018 and 2023 Survey data. We put our focus on comparisons of different aspects of programmerâs life with respect to salary. In particular, we will explore
In this section we will compare the distribution of salaries in 2018 and 2023 and ask an important question: do programmers earn more in 2023 than in 2018?
First, we investigate how salary changed over the six years:
Apart from the obvious fact that salary increases with the experience, we observe and interesting decline in more experienced professionalist.
Comparing salaries in 5 years period we cannot forget about inflation. As salaries have been converted to USD by the StackOverflow at the time, we can take only dollarâs inflation into the account
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Itâs important to remark that we the the conversion rates between currencies do not depend only Inflation.
After consideration we may wonder how the changes relate to the inflation.
The total cumulative inflation betwen 2018 and 2023 was???? 0.242769
We now examine how percentage-wise changes in compensation, compare to the inflation
As we can see, even though the nominal salary increased in for developers up to 23 years of experience, the only group that earns more after these 5 years are new developers.
Now we will examine, if languages that are especially liked by programmers, bring them more income
As we can see we obtain a triangular shape. Langugages, which developers
want to use the most, are in the middle of salaries.
On the other hand the less desired languages spread accros the salary ax. On the one hand, systems that use egsotic???? and unpleasent languages, will pay moreto attract employees, and on the other hand âŠ
We wanted to examine if at different stages of professional life it is more worth it to work in small companies or big corporations.
## `summarise()` has grouped output by 'CompanySize'. You can override
## using the `.groups` argument.
## `summarise()` has grouped output by 'CompanySize'. You can override
## using the `.groups` argument.
Interestingly, it seems that working in big giants is always most
profitable. On the other hand, the more experience you have the more
plot buldge around 500-999 employees.
Of course salary depends greatly on the country you work with.
## Warning in geom_map(data = world_data, map = world_data, aes(x = long, y
## = lat, : Ignoring unknown aesthetics: x and y
As
shown above, the countries which stand out are USA, switzerland and
Australia